Open AccessApplication of multivariate statistical techniques in the evaluation of large-scale water treatment plants in Baghdad.
Author(s) -
Nisreen Y. Mohammed,
Khalid Adel Abdulrazzaq
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1105/1/012109
Subject(s) - principal component analysis , water quality , multivariate statistics , environmental science , raw water , nonparametric statistics , water treatment , mathematics , statistics , environmental engineering , ecology , biology
This paper aims to evaluate large-scale water treatment plants’ performance and demonstrate that it can produce high-level effluent water. Raw water and treated water parameters of a large monitoring databank from 2016 to 2019, from eight water treatment plants located at different parts in Baghdad city, were analyzed using nonparametric and multivariate statistical tools such as principal component analysis (PCA) and hierarchical cluster analysis (HCA). The plants are Al-Karkh, Sharq-Dijlah, Al-Wathba, Al-Qadisiya Al-Karama, Al-Dora, Al-Rasheed, Al-Wehda. PCA extracted six factors as the most significant water quality parameters that can be used to evaluate the variation in drinking water quality and responsible for 73.389% of the variance in the data set. Based on this selection criterion, the most significant water quality parameters that can be used to evaluate the variation in drinking water quality parameters are the mineral-related parameters (e.g., Ca +2 , Mg +2 , salinity, hardness), the nutrient parameters (i.e., dissolved nitrate and nitrite and orthophosphate), and a physical parameter. HCA analysis was able to group water treatment plants with similar raw water and treated water quality based on the water quality data from eight WTPs into three clusters.